The Tech Industy in Los Angeles City and County
Joao de Paula
2018 Nov 26
1 City
PHOTO BY TYLER MERBLER
Los Angeles, CA has a population of 3.98M people with a median age of 35.6 and a median household income of $54,432. Between 2015 and 2016 the population of Los Angeles, CA grew from 3.97M to 3.98M, a 0.11% increase and its median household income grew from $52,024 to $54,432, a 4.63% increase.
2 County
PHOTO BY PAOLO GAMBA
Los Angeles County, CA has a population of 10.1M people with a median age of 36.3 and a median household income of $61,338. Between 2015 and 2016 the population of Los Angeles County, CA declined from 10.2M to 10.1M, a 0.32% decrease and its median household income grew from $59,134 to $61,338, a 3.73% increase.
LA is a tech hub, with $ x ,000 tech jobs roughhty x% of the nation …. COmpanies like Amazon, Tesla, etc …have headquarters in Los Angeles. It is a vibrant segment of the economy. While California is the 7th economy in the world, Los Angeles is the 24th economy in the world bigger than countries like ….
Let’s take a look at the tech industry employment [1] numbers for the past 13 years.
3 Employment Projections
# add geom_hline showing "Total media wages"
# # Change the "horizontal justification" to be left-justified with hjust
mycols <- c("blue", "red")
ggplot(data=occupation, mapping=aes(x=industry, y=median_wages17, fill=compu)) +
stat_summary(fun.y=mean, geom="bar") +
scale_y_continuous(labels=dollar) +
labs(title="Median Wages by Industry in 2017", x="", y="") +
coord_flip() +
theme(legend.position="none") +
scale_fill_manual(values=mycols) +
geom_text(aes(label = median_wages17), color = "black", hjust=-0.1) +
geom_hline(yintercept = 37690, color="red", size=1, linetype="dashed")
# remove all occupations
occupation <- occupation[2:23,]
occupation$industry <- fct_reorder(occupation$industry, occupation$change2026, mean, na.rm=TRUE)
ggplot(data=occupation, mapping=aes(x=industry, y=change2026, fill=compu)) +
stat_summary(fun.y=mean, geom="bar") +
scale_y_continuous() +
labs(title="Employment Jobs Growth Projection by Industry 2016-2026 ", x="", y="") +
coord_flip() +
theme(legend.position="none") +
scale_fill_manual(values=mycols) +
geom_text(aes(label = change2026), color = "black", hjust=-0.1) 
occupation$industry <- fct_reorder(occupation$industry, occupation$perchange206, mean, na.rm=TRUE)
ggplot(data=occupation, mapping=aes(x=industry, y=perchange206, fill=compu)) +
stat_summary(fun.y=mean, geom="bar") +
scale_y_continuous() +
labs(title="% Employment Growth Projection by Industry 2016-2026 ", x="", y="") +
coord_flip() +
theme(legend.position="none") +
scale_fill_manual(values=mycols) +
geom_text(aes(label = perchange206), color = "black", hjust=-0.1)
2017 Employment by major occupational group, 2016 and projected 2026 (Numbers in thousands) [1]. Tech employees are compensated nicely due to this tech boom. For example, in 207 while the annual medialn wage for all occupations was $37,000 in the U.S., it was $84,600 for the computer occupations, second only to $102,000 of the management occupations as shown in Table 1.
4 Dinamic Visualizations
Figure 1 shows the total jobs in the city of Los Angeles and County.
# install imageMagick
# https://www.imagemagick.org/script/download.php#windows
# Tutorials
# https://ryanpeek.github.io/2016-10-19-animated-gif_maps_in_R/
# https://cran.r-project.org/web/packages/magick/vignettes/intro.html
# library(magick) # this is call to animate/read pngs
# save all png files to path folder
# create gif
# list.files(path = "C:/Users/joao_/Downloads/Gif/", pattern = "*.png", full.names = T) %>%
# map(image_read) %>% # reads each path file
# image_join() %>% # joins image
# image_animate(fps=100) %>% # animates, can opt for number of loops
# image_write("tech_100.gif") # write to current dir
Figure 2 shows the evolution of Tech Jobs by zip code from 2012 to 2017
5 Maps
## OGR data source with driver: ESRI Shapefile
## Source: "C:\Users\joao_\Dropbox\Data Science\UCLA\Git\los-angeles-tech-jobs\data\zip-codes\geo_export_1887acad-adf8-4181-8c7d-50a16a3435f2.shp", layer: "geo_export_1887acad-adf8-4181-8c7d-50a16a3435f2"
## with 311 features
## It has 4 fields## objectid shape_area shape_len zipcode
## 0 1 95563402 48677.28 90001
## 1 2 82752224 44972.02 90002
## 2 3 102660027 63294.58 90003
## 3 4 83956967 52471.90 90004
## 4 5 36897792 88445.00 90005
## 5 6 53707686 37830.42 90006## [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [12] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [23] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [34] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [45] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [56] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
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## [78] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
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## [100] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [111] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
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## [133] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [144] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [155] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [166] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [177] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [188] TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [199] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [210] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [221] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [232] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [243] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [254] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [265] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [276] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [287] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [298] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [309] TRUE TRUE TRUE## [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [15] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [29] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [43] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [57] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [71] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [85] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [99] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [113] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [127] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [141] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [155] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [169] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [183] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [197] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [211] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [225] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [239] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [253] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [267] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [281] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [295] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [309] TRUE TRUE TRUE## 'data.frame': 311 obs. of 4 variables:
## $ objectid : num 1 2 3 4 5 6 7 8 9 10 ...
## $ shape_area: num 9.56e+07 8.28e+07 1.03e+08 8.40e+07 3.69e+07 ...
## $ shape_len : num 48677 44972 63295 52472 88445 ...
## $ zipcode : Factor w/ 311 levels "90001","90002",..: 1 2 3 4 5 6 7 8 9 10 ...## objectid shape_area shape_len zipcode
## 0 1 95563402 48677.28 90001
## 1 2 82752224 44972.02 90002
## 2 3 102660027 63294.58 90003
## 3 4 83956967 52471.90 90004
## 4 5 36897792 88445.00 90005
## 5 6 53707686 37830.42 90006## 'data.frame': 311 obs. of 8 variables:
## $ GEOID: chr "90001" "90002" "90003" "90004" ...
## $ V1 : num 5545 5546 5547 5548 5549 ...
## $ year : num 2017 2017 2017 2017 2017 ...
## $ tech : num 96 5 33 784 606 ...
## $ info : num 19 0 0 224 279 77 71 111 860 0 ...
## $ prof : num 77 5 33 560 327 ...
## $ per : num 1.03 0.15 0.49 7.61 8.88 ...
## $ total: num 9334 3320 6720 10296 6824 ...## [1] "objectid" "shape_area" "shape_len" "zipcode"
Fig. X shows dot density for all tech jobs in LA county. Each dot = 10 people
6 Correlation Between the CHCI and the Median House Income of Los Angeles County Zip Codes
Fig. 8 shows the association between the 2012 CHCI and the 2016 median household income of 282 zip codes in L.A. County. We can see a very strong correlation correlation between the human capital level [23] and the household income for the zip code in L.A. It demonstrates the importance to invest in education (chci) in order to create more productive workforce and more high paying jobs.
Source - 5-year American Community Survey 2012-2016 Global Innovation Index 2018, World Bank Population Data 2016, Q11 - Current country of residence [19-21]
7 Conclusions
- Gender imbalance continues (Fig. 1)
- BRICS to outnumber Western countries by 2020 (Fig. 6)
- User prevalence moderatedly-correlated with innovation (Fig. 8)
8 References
- [1] Assumptions Tech industry employment was calculated using the following: (1) Information jobs (NAICS: 51), and (2) Professional, Scientific, & Technical Skills (NAICS: 54). Data source: Quarterly Census of Employment and Wages (QCEW) developed through a cooperative program between the states and the U.S. Bureau of Labor Statistics.– These data are summarized by Industry Sector (2-digit NAICS).
- [2] https://www.bls.gov/emp/tables/emp-by-major-occupational-group.htm
Shape Files - https://data.lacity.org/ Los Angeles Open Data ACS
9 Acknowledgements
- This project was largely inspired by conversations with Dr. William Yu, UCLA
- Dynamic Visualizations were inspire by Google Public Data Explorer.
- Fig. 3 and 5 was largely inspired by conversations with Ferran Pujol of McKinsey &Co. Chile.
- Fig. 6 inspired by the charts of @ash316 [15]
- Fig. 1,2,3 inspired by the charts of @paultimothymooney [4]
- Hadley Wickham, for his development of ggplot2 (Fig 7, 8), the open-source statistical analysis & data visualisation based on Leland Wilkinson’s book Grammar of Graphics.
https://geocompr.robinlovelace.net/index.html
#googleWalkout [2] and is bad business - says Forbes [3]. In Fig. 1 (above), we use superhero-themes #batman #wonderwoman to visualize the heavy topic of #gender_equality in #datascience. See a bar chart for a more accurate breakdown [4]. Source: survey question Q1 - What is your gender? Sample size = 23,859 respondents
- [2] https://www.theverge.com/2018/11/2/18057716/google-walkout-20-thousand-employees-ceo-sundar-pichai-meeting
- [3] https://www.forbes.com/sites/womensmedia/2017/08/03/breaking-down-the-gender-gap-in-data-science/#129d1bb74287
- [4] https://www.kaggle.com/paultimothymooney/2018-kaggle-machine-learning-data-science-survey
- [5] https://en.wikipedia.org/wiki/Generations_in_the_workforce
- [6] Sinton, E (2011). ‘Baby boomers are very privileged human beings’ https://www.telegraph.co.uk/finance/personalfinance/pensions/8840963/Baby-boomers-are-very-privileged-human-beings.html retrieved October 23, 2013 from www.telegraph.co.uk
- [7] Ken Blanchard Companies. (2009). Next Generation of workers. http://www.kenblanchard.com/img/pub/Blanchard_Next_Generation_of_Workers.pdf Retrieved October 14, 2013, from kenblanchard.com
- [8] Adecco Group UK and Ireland. (n.d.). Managing the modern workforce. http://www.adeccogroupuk.co.uk/SiteCollectionDocuments/Adecco-Group-Workplace-Revolution.pdf Retrieved October 13, 2013, from www.Adeccouk.co.uk
- ref. needed
- [10] https://en.wikipedia.org/wiki/Affluence_in_the_United_States
- [11] https://www.epi.org/blog/top-1-0-percent-reaches-highest-wages-ever-up-157-percent-since-1979/
- [12] J. Berengueres, Sketch thinking. 2016
- [13] https://en.wikipedia.org/wiki/Marimekko#Marimekko_chart
- [14] ref. needed
- [15] https://www.kaggle.com/ash316/kaggle-journey-2017-2018
- [16] https://en.wikipedia.org/wiki/BRICS
- [17] https://www.kaggle.com/harriken/brics-growth
- [18] See primary vs. secondary color in https://material.io/design/color/the-color-system.html#color-theme-creation
- [19] Dutta, S., Reynoso, R.E., Garanasvili, A., Saxena, K., Lanvin, B., Wunsch-Vincent, S., Le?n, L.R. and Guadagno, F., 2018. THE GLOBAL INNOVATION INDEX 2018: ENERGIZING THE WORLD WITH INNOVATION. GLOBAL INNOVATION INDEX 2018, p.1.
- [20] CSV file global innovation in https://www.globalinnovationindex.org/analysis-indicator
- [21] World Bank, https://data.worldbank.org/indicator/SP.POP.TOTL
- [22] https://en.wikipedia.org/wiki/Kendall_rank_correlation_coefficient
The shapefile format is a common way to store data for geographic areas, namely polygons, lines, and points. Each tract is represented as a polygon (i.e. some shape)
Dowload shape files for county from LA Data Portal https://data.lacounty.gov/Geospatial/ZIP-Codes/65v5-jw9f